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Python Rapid
Artificial Intelligence
Ab Initio Molecular Dynamics
Author @Jingbai Li
Northeastern University, Boston, USA
version: 2.1 alpha
With contriutions from (in alphabetic order):
Jingbai Li - Fewest switches surface hopping
Zhu-Nakamura surface hopping
Velocity Verlet
OpenMolcas interface
OpenMolcas/Tinker interface
BAGEL interface
Adaptive sampling
Grid search
Two-layer ONIOM (coming soon)
Periodic boundary condition (coming soon)
QC/ML hybrid NAMD
Patrick Reiser - Neural networks (pyNNsMD)
Special acknowledgement to:
Steven A. Lopez - Project directorship
Pascal Friederich - ML directoriship>
- Machine learning nonadibatic molecular dyanmics (ML-NAMD).
- Neural network training and grid search.
- Active learning with ML-NAMD trajectories.
- Support BAGEL, Molcas for QM, and Molcas/Tinker for QM/MM calculations.
- Generalized FSSH and ZNSH with nonadibatic coupling and spin-orbit coupling
- Add curvature-driven time-depedent coupling for FSSH
- Python >=3.7 PyRAI2MD is written and tested in Python 3.7.4. Older version of Python is not tested and might not be working properly.
- TensorFlow >=2.2 TensorFlow/Keras API is required to load the trained NN models and predict energy and force.
- Cython PyRAI2MD uses Cython library for efficient surface hopping calculation.
- Matplotlib/Numpy Scientifc graphing and numerical library for plotting training statistic and array manipulation.
File/Folder Name Description
---------------------------------------------------------------------------------------------------
pyrai2md.py PyRAI2MD interface
PyRAI2MD source codes folder
|--variables.py PyRAI2MD input reader
|--method.py PyRAI2MD method manager
|--Molecule atom, molecule, trajectory code folder
| |--atom.py atomic properties class
| |--molecule.py molecular properties class
| |--trajectory.py trajectory properties class
| |--pbc_helper.py periodic boundary condition functions
| `-qmmm_helper.py qmmm functions
|
|--Quantum_Chemistry quantum chemicial program interface folder
| |--qc_molcas.py OpenMolcas interface
| |--qc_bagel.py BAGEL interface
| `-qc_molcas_tinker OpenMolcas/Tinker interface
|
|--Machine_Learning machine learning library interface folder
| |--training_data.py training data manager
| |--model_NN.py neural network interface
| |--hypernn.py hyperparameter manager
| |--permutation.py data permutation functions
| |--adaptive_sampling.py adaptive sampling class
| |--grid_search.py grid search class
| |--remote_train.py distribute remote training
| `-pyNNsMD neural network library
|
|--Dynamics ab initio molecular dynamics code folder
| |--aimd.py molecular dynamics class
| |--mixaimd.py ML-QC hybrid molecular dynamics class
| |--single_point.py single point calculation
| |--hop_probability.py surface hopping probability calculation
| |--reset_velocity.py velocity adjustment functions
| |--verlet.py velocity verlet method
| |--Ensembles thermodynamics control code folder
| | |--ensemble.py thermodynamics ensemble manager
| | |--microcanonical.py microcanonical ensemble
| | `-thermostat.py canonical ensemble
| |
| `-Propagators electronic propagation code folder
| |--surface_hopping.py surface hopping manager
| |--fssh.pyx fewest switches surface hopping method
| |--gsh.py generalized surface hopping method
| `-tsh_helper.py trajectory surface hopping tools
|
`-Utils utility folder
|--aligngeom.py geometry aligment and comparison functions
|--coordinates.py coordinates writing functions
|--read_tools.py index reader
|--bonds.py bond length library
|--sampling.py initial condition sampling functions
|--timing.py timing functions
`-logo.py logo and credits
Download the repository
git clone https://github.com/lopez-lab/PyRAI2MD.git
Specify environment variable of PyRAI2MD
export PYRAI2MD=/path/to/PyRAI2MD
Compile fssh library
cd $PYRAI2MD/Tools
bash compile_cython_fssh.sh
Copy the test script and modify environment variables
cp $PYRAI2MD/Tools/test_PyRAI2MD.sh .
bash test_PyRAI2MD.sh
Or directly run if environment variables are set
$PYRAI2MD/pyrai2md.py quicktest
$PYRAI2MD/pyrai2md.py input
We are currently working on the user manual.
- Jingbai Li, Patrick Reiser, Benjamin R. Boswell, André Eberhard, Noah Z. Burns, Pascal Friederich, and Steven A. Lopez, "Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations", Chem. Sci. 2021, 12, 5302-5314. DOI: 10.1039/D0SC05610C
- Jingbai Li, Rachel Stein, Daniel Adrion, Steven A. Lopez, "Machine-learning photodynamics simulations uncover the role of substituent effects on the photochemical formation of cubanes", J. Am. Chem. Soc. 2021, 143, 48, 20166–20175. DOI:10.1021/jacs.1c07725